Abstract

In this chapter, we address the visual learning of automatic concept detectors from web video as available from services like YouTube. While allowing a much more efficient, flexible, and scalable concept learning compared to expert labels, web-based detectors perform poorly when applied to different domains (such as specific TV channels). We address this domain change problem using a novel approach, which - after an initial training on web content - performs a highly efficient online adaptation on the target domain.
In quantitative experiments on data from YouTube and from the TRECVID campaign, we first validate that domain change appears to be the key problem for web-based concept learning, with much more significant impact than other phenomena like label noise. Second, the proposed adaptation is shown to improve the accuracy of web-based detectors significantly, even over SVMs trained on the target domain. Finally, we extend our approach with active learning such that adaptation can be interleaved with manual annotation for an efficient exploration of novel domains.